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EDITORIAL

Guest Editorial Special Issue on the Next-Generation Deep Learning Approaches to Emerging Real-World Applications

Yu Zhou1, Eneko Osaba2, Xiao Zhang3,*

1 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
2 TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, 48160, Spain
3 Department of Computer Science, South-Central Minzu University, Wuhan, 430074, China

* Corresponding Author: Xiao Zhang. Email: email

(This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)

Computers, Materials & Continua 2025, 84(1), 237-242. https://doi.org/10.32604/cmc.2025.066663

Abstract

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Cite This Article

APA Style
Zhou, Y., Osaba, E., Zhang, X. (2025). Guest Editorial Special Issue on the Next-Generation Deep Learning Approaches to Emerging Real-World Applications. Computers, Materials & Continua, 84(1), 237–242. https://doi.org/10.32604/cmc.2025.066663
Vancouver Style
Zhou Y, Osaba E, Zhang X. Guest Editorial Special Issue on the Next-Generation Deep Learning Approaches to Emerging Real-World Applications. Comput Mater Contin. 2025;84(1):237–242. https://doi.org/10.32604/cmc.2025.066663
IEEE Style
Y. Zhou, E. Osaba, and X. Zhang, “Guest Editorial Special Issue on the Next-Generation Deep Learning Approaches to Emerging Real-World Applications,” Comput. Mater. Contin., vol. 84, no. 1, pp. 237–242, 2025. https://doi.org/10.32604/cmc.2025.066663



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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